In a very general sense, knowledge systems can be grouped according to whether the primary purpose is for defining or checking equipment or processes, or for supporting a user in the tasks of learning, information retrieval, or problem-solving.
An example of the former type of knowledge system is U.S. Pat. No. 4,591,983, "Hierarchical Knowledge System", which describes a hierarchical knowledge base comprising a functional decomposition of a set of elements into subsets over a plurality of hierarchical levels, a plurality of predefined functions or conditions of the elements within the subsets of a plurality of the hierarchical levels, and a predefined set of operations to perform on a user-defined set of elements responsive to the functional knowledge base. Although claimed therein as a knowledge system comprising a computer having a memory storing a particular type of knowledge base, the preferred embodiment relates generally to inventory control and the processing of orders for flexibly assembling systems or items of manufacture, and more specifically to computer systems to aid in the checking of orders for products or systems to be manufactured or assembled.
An example of the other general type of knowledge system is represented by U.S. Pat. No. 4,648,037, "Method and Apparatus for Benefit and Financial Communication", which discloses and claims a system and method for making available financial and employee benefit information to any one individual of a group of individuals who are members of an employer's benefits plan. The system enables an employee to interactively access information via a terminal, concerning their savings plans, withdrawal information, explanations of provisions, employee benefit information, explanation of savings plan and benefit options, and benefit news bulletins. Thus, the second example is not utilized for controlling equipment or another process, but merely for end-user information retrieval and for personal financial problem-solving.
As the number of knowledge or information users in the workplace increases, systems that can serve as reference or help users learn job tasks, specifically information processing tasks, have evolved to address three specific areas: information distribution, employee development and improved decision-making. Primarily implemented in an off-the-job context on mainframe systems, they are known respectively as on-line reference, computer-based training and problem-solving systems.
On-line reference systems are essentially an automation of flipping through a manual. Just as one might use a table of contents and separator tabs to flip to specific information in a three-ring notebook, an on-line reference system provides menu-selections to tab down through a sequentially organized information file to retrieve and display the requested information. Such automation was designed to eliminate the costly problems of publishing and distributing the manual, and by storing it in one central resource, to eliminate the inaccuracy and inconsistency of information that results when individual pages are never updated. Because of the cost savings, many organizations eventually install several such systems, each to replace an existing manual. Since they are not cross-referenced and these individual systems are generally updated by the different departments who "own" them, the old problems of duplication and inconsistency that characterized multiple hard-copy manuals, often resurface. Other problems include content display that is difficult to read. Although studies have shown that reading a screen takes 30% longer than reading a hard-copy page, most reference systems are installed as verbatim copies of the paper bound manuals they replaced. In order to save cost and decrease the period of time required for implementation, many on-line reference systems were implemented by digital scanning or merely re-keying from hard copy. Moreover, while some may allow for rudimentary connections to other systems or remote data, cross-references and/or real integration with other systems or data is precluded due to the single-purpose, flat-file, sequential structures. To the extent cross-referencing between different knowledge content files has been attempted, this has normally been achieved by complex programming techniques which are not generalized and need skilled maintenance.
Another form of knowledge transmission has been computer-based training. These systems have typically been designed for individualized self-study by following a system-controlled sequence of presenting content, testing knowledge levels, suggesting remedial activity, and repeating lessons where necessary. A clear disadvantage, however, is that the physical structure (flat-file, sequential) and design methodology creates severe limitations: (1) It precludes any ability to address individual informational or learning style needs, e.g., the same content is presented to all, whether needed or not, pictures and text can only be presented where programmed to do so, and content can be geared to only one "average" skill level. The inherent flaws here are that there are no homogeneous audiences when relating to skill and knowledge levels, learning styles and job need. Further, people learn when they can control how and when they learn, e.g., in the sequence and the media that makes sense to them, not in the media, sequence and skill level of the instructor-developer. (2) Traditional course development methods are so tedious and time-consuming that course content is often obsolete by the time it has been developed. (3) Since training is usually a scheduled event, and not available at the time of actual need, the content learned but not applied within forty-eight hours is usually forgotten. (4) High development and maintenance costs, especially as compared to the actual developmental playback, remain a serious problem. (5) Finally, the sequential, flat-file structure precludes useful, maintainable integration with other systems or data.
Automated problem-solving systems generally fall into two different types. The inexpensive, simpler type uses a tree structure to narrow down a problem to the point whereby the system makes a specific recommendation to the user. While this provides an advantage of quick assistance in the solution of known, simple problems, the technique is also inherently system, not user-driven, which provides clear disadvantages to users: (1) It provides for little learning as the data analysis and decision-making is automated. (2) The value of the system is limited to the conditions and problems known to the programmer at the time. (3) Maintenance requires reprogramming. The other type, known as expert systems, perform the same functions with the same attendant problems but with increased severity because of its increased complexity. The heavy integration of complex logic with rules for professional application of complex knowledge is very difficult if not impossible to program. It requires the programmer to be as skilled in the expert knowledge and its application as in programming, and for all possible conditions and variables of complex problems to be predefined with an action related to each. Because of these impossible barriers, only a few real expert systems are working satisfactorily, at a cost of many millions for development and maintenance.